![](/images/graphics-bg.png)
Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information
المؤلفون المشاركون
Yu, Xinghao
Xiao, Lishun
Zeng, Ping
Huang, Shuiping
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-04-08
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
Motivation.
In the past few years many prediction approaches have been proposed and widely employed in high dimensional genetic data for disease risk evaluation.
However, those approaches typically ignore in model fitting the important group structures that naturally exists in genetic data.
Methods.
In the present study, we applied a novel model-averaging approach, called jackknife model averaging prediction (JMAP), for high dimensional genetic risk prediction while incorporating pathway information into the model specification.
JMAP selects the optimal weights across candidate models by minimizing a cross validation criterion in a jackknife way.
Compared with previous approaches, one of the primary features of JMAP is to allow model weights to vary from 0 to 1 but without the limitation that the summation of weights is equal to one.
We evaluated the performance of JMAP using extensive simulation studies and compared it with existing methods.
We finally applied JMAP to four real cancer datasets that are publicly available from TCGA.
Results.
The simulations showed that compared with other existing approaches (e.g., gsslasso), JMAP performed best or is among the best methods across a range of scenarios.
For example, among 14 out of 16 simulation settings with PVE = 0.3, JMAP has an average of 0.075 higher prediction accuracy compared with gsslasso.
We further found that in the simulation, the model weights for the true candidate models have much smaller chances to be zero compared with those for the null candidate models and are substantially greater in magnitude.
In the real data application, JMAP also behaves comparably or better compared with the other methods for continuous phenotypes.
For example, for the COAD, CRC, and PAAD datasets, the average gains of predictive accuracy of JMAP are 0.019, 0.064, and 0.052 compared with gsslasso.
Conclusion.
The proposed method JMAP is a novel model-averaging approach for high dimensional genetic risk prediction while incorporating external useful group structures into the model specification.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yu, Xinghao& Xiao, Lishun& Zeng, Ping& Huang, Shuiping. 2019. Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1130506
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yu, Xinghao…[et al.]. Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1130506
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yu, Xinghao& Xiao, Lishun& Zeng, Ping& Huang, Shuiping. Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1130506
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130506
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)